As a primary means of information acquisition, information retrieval (IR) systems, such as
search engines, have integrated themselves into our daily lives. These systems also serve …

Ranking documents using Large Language Models (LLMs) by directly feeding the query and
candidate documents into the prompt is an interesting and practical problem. However …

We study ranked list truncation (RLT) from a novel retrieve-then-re-rank perspective, where
we optimize re-ranking by truncating the retrieved list (ie, trim re-ranking candidates). RLT is …

Large Language Models (LLMs) demonstrate robust capabilities across various fields,
leading to a paradigm shift in LLM-enhanced Recommender System (RS). Research to date …

Query performance prediction (QPP) aims to estimate the retrieval quality of a search system
for a query without human relevance judgments. Previous QPP methods typically return a …

Recently, there has been increasing interest in applying large language models (LLMs) as
zero-shot passage rankers. However, few studies have explored how to select appropriate …

Large Language Models (LLMs) have shown strong in-context learning (ICL) abilities with a
few demonstrations. However, one critical challenge is how to select demonstrations to elicit …

Information retrieval (IR) systems have played a vital role in modern digital life and have
cemented their continued usefulness in this new era of generative AI via retrieval …

Synthetic data has emerged as a critical component in the fields of machine learning and
data science, providing a solution to overcome limitations associated with real-world data …

Text embedding methods have become increasingly popular in both industrial and
academic fields due to their critical role in a variety of natural language processing tasks …